Staff ML Engineer, Generative AI Applications

Posted 5 Days Ago
Hiring Remotely in United States
Remote
5-7 Years Experience
Fintech
The Role
Design and build machine models for risk, fraud, recommendation, and personalization in GenAI applications. Automate ML pipelines, establish frameworks for deployment and monitoring, build guardrails, and mentor team members. Collaborate with Product Managers on algorithmic performance.
Summary Generated by Built In

Bolt is on a mission to democratize commerce. We relentlessly prioritize our retailers—putting their brands front and center while enabling frictionless shopping at any touchpoint in the customer journey. At the center of it all is our rapidly growing universal shopper network—Bolt merchants such as Deckers, Saks OFF 5TH, Revolve, and Casper can access tens of millions of shoppers, offering them a best-in-class checkout.

And revolutionizing ecommerce is only half of the equation—we’re also transforming the way we work. At Bolt, we have created a work environment where people learn to drive impact, take risks and make big bets, and grow from feedback, all while feeling welcomed and accepted for who they are. Come join us on the adventure today!

The Data Platform team works closely with all teams and cross-functional partners (including Product, Engineering, and Data Analysts) to build a foundational data stack powering business analytics.

We are looking for someone to play a mission-critical role in designing and building the machine models for risk, fraud, recommendation, and personalization that powers Bolt. This should be someone with experience, creativity, and passion for producing world-class technology. Companies and consumers alike will rely heavily on what you build, and you’ll have a ton of trust and responsibility. If challenges excite you, and you’re ready for a large one, let us know.

What you will be doing:

  • Architect, build, maintain, and improve new and existing suite of GenAI applications and their underlying systems
  • Automate machine learning pipelines, monitor performance and costs, and optimize models by using techniques such as LoRA/QLoRA
  • Establish reusable frameworks to streamline model building, deployment and monitoring, incorporating comprehensive monitoring, logging, tracing, and alerting mechanisms
  • Build guardrails, compliance rules and oversight workflows into the GenAI application platform, such as establishing approval chains for model updates and staged rollout for production releases
  • Ensure development of user-facing applications in the GenAI application platform is easy and safe by enforcing rigorous validation testing before publishing user-generated models and implement a clear peer review process of applications
  • Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code
  • Collaborate with Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact

What would set you up for success:

  • Minimum of seven years’ post-secondary education or relevant work experience, along with minimum of five years’ software development experience with Python and SQL
  • Minimum of three years’ experience using PyTorch, Tensorflow, building pipelines to deploy NLP and deep learning models into production in a cloud (AWS, GCP, Azure) environment
  • Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using LangChain and similar tools
  • Experience in establishing model guardrails and developing bias detection and mitigation techniques for AI applications using tools such as NeMo
  • Experience with various embedding models and setting up and tuning vector databases to improve performance of semantic search and retrieval systems
  • Understand the underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs
  • Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation

What would set you apart:

  • Retail and/or Martech experience in building large-scale personalization and recommendation systems in a consumer-based setting.
  • Experience with big data tools like Spark, Kafka, BigQuery, Dataflow, Apache Beam, Pubsub, Cloud Functions, EMR, S3, Glue, Kinesis Firehose, Lambda; a Linux environment

Estimated Cash Compensation: $230k-$280k plus equity, DOE

Benefits:

  • Comprehensive health coverage: Medical, dental and vision
  • Remote-first workplace
  • Time away: Flexible PTO, paid holidays + floating holidays, your birthday off!
  • Paid parental leave
  • Competitive Pay
  • Retirement plans
  • Virtual and in-person team & company events

In addition to our core values, Bolt is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity and expression, genetic information, pregnancy and related conditions, veteran status or any other reason prohibited by law. On our mission to democratize commerce, the Bolt platform levels the playing field for everyone. As a company, we are committed to designing products, building a culture, and supporting a team that reflects the diverse population we serve (that is, everyone).

Top Skills

Python
SQL
The Company
HQ: San Francisco, CA
365 Employees
Hybrid Workplace
Year Founded: 2014

What We Do

Bolt is the world’s first checkout experience platform. Our checkout is optimized for any device and platform and has seamless fraud-detection built-in. We enable customer-obsessed brands to delight shoppers, elevate their brand experience, and mitigate the number of fraudulent transactions.

Why Work With Us

We’re building more than just a platform. We’re changing the way shoppers purchase online. We’re driven by our values - not only do they guide our work, but they also dictate how we treat one another. We don’t shy away from candid feedback, we welcome it; we don’t win as individuals, we win as a team.

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